Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-19 DOI:10.1088/1741-2552/acec14
Nitin Sadras, Omid G Sani, Parima Ahmadipour, Maryam M Shanechi
{"title":"Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making.","authors":"Nitin Sadras,&nbsp;Omid G Sani,&nbsp;Parima Ahmadipour,&nbsp;Maryam M Shanechi","doi":"10.1088/1741-2552/acec14","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective.</i>When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.<i>Approach.</i>We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.<i>Main results.</i>We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.<i>Significance.</i>Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.</p>","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":"20 5","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1741-2552/acec14","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 1

Abstract

Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
脑电决策置信度的后刺激编码:面向决策的脑机接口。
客观。在做出决定时,人类可以评估他们正确的可能性。如果这种主观置信度能够从大脑活动中可靠地解码,那么就有可能建立一个脑机接口(BCI),通过根据用户的置信度在需要时自动向用户提供更多信息来提高决策性能。但这种可能性取决于是否可以在刺激出现后和反应前立即解码置信度,以便及时采取纠正措施。尽管先前的工作已经表明决策置信度在大脑信号中表示,但尚不清楚该表示是刺激锁定还是反应锁定,以及刺激锁定预反应解码是否足够准确,以实现这种脑机接口方法。我们通过在具有现实刺激的感知决策任务中收集高密度脑电图(EEG)来研究置信度的神经相关性。重要的是,我们将我们的任务设计为包括一个刺激后缺口,以防止刺激锁定活动与反应锁定活动混淆,反之亦然,然后与没有这个缺口的任务进行比较。主要结果。我们进行事件相关电位和源定位分析。我们的分析表明,信心的神经相关性是刺激锁定的,刺激后间隙的缺失可能会导致这些相关性错误地显示为反应锁定。通过防止反应锁定活动混淆刺激锁定活动,我们证明了可以从单独的试验刺激锁定反应前脑电图中可靠地解码置信度。我们还通过比较一组算法来确定一种高性能的分类算法。最后,我们设计了一个模拟的脑机接口框架,以表明EEG分类足够准确,可以建立脑机接口,并且解码的置信度可以用来提高决策性能,特别是在任务难度和错误成本较高的情况下。意义。我们的研究结果表明了基于脑电的无创脑机接口改善人类决策的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
期刊最新文献
Building consensus on clinical outcome assessments for BCI devices. A summary of the 10th BCI society meeting 2023 workshop. o-CLEAN: a novel multi-stage algorithm for the ocular artifacts' correction from EEG data in out-of-the-lab applications. PDMS/CNT electrodes with bioamplifier for practical in-the-ear and conventional biosignal recordings. DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness. I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1